When a wavelet transform is performed for a time-series signal representing vibrations, sounds, process data, or the like by using a complex type wavelet function, information about a time-frequency domain can be obtained. When intensities (absolute values) of this time-frequency domain information are computed, changes in various frequency characteristics contained in the time-series signal over time can be analyzed.
There is a technique (reference 1: Japanese Patent Laid-Open No. 7-271763) of differentiating time-frequency information as a wavelet transform result in units of scales, extracting extreme values as feature amounts, and using them for analysis and diagnosis.
There is a technique (reference Japanese 2: Patent Laid-Open No. 8-83265) of extracting feature amounts representing the periodicity of a signal, in units of scales, from time-frequency information as a wavelet transform result serving as a target signal and using them for analysis and diagnosis.
There is a technique (reference 3: Japanese Patent Laid-Open No. 8-219955) of computing statistical amounts such as averages and variances from a wavelet transform result serving as a target signal in units of scales, extracting them as features, and using them for diagnosis.
There is a technique (reference 4: Japanese Patent Laid-Open No. 8-177530) of comparing a wavelet transform result serving as a target signal with a predetermined threshold in units of scales, extracting values exceeding the threshold as features, and using them for diagnosis.
There is a technique (reference 5: Japanese Patent Laid-Open No. 8-329046) of computing variances in units of scales from a wavelet transform result serving as a target signal, extracting the peaks of the obtained variance distributions as features, and using them for analysis.